Discussion on the Design of Food Safety Subject Database Based on Big Data Testing

• Published in: E3S web of conferences Vol. 292; p. 2052
• Main Authors: Chen, Kai, Tao, Heng, Yu, Jie, Hao, Miao, Yang, Hong, Wang, Yajie
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01.01.2021
• Subjects: Big Data; Food industry; Food processing; Food safety; Optimization; Regulatory agencies; Safety; Safety engineering; Safety regulations; Supervision
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202129202052

Food safety is a matter of national importance, and it is important to establish and improve a whole process food safety supervision system with high standard. Along with the advent of the era of big data, to achieve this goal requires not only the reform and optimization of the old regulatory approach needs to establish the idea of information-based supervision, and actively apply big data technology to food safety testing. In this paper, we introduce the current situation of food safety in society, analyse the shortcomings of current food safety supervision and the challenges faced in the era of big data, discuss the feasibility of using big data for food safety testing with big data technology as the core, and give the design idea of establishing a corresponding food safety subject database. This paper takes food safety testing as the foothold and innovatively combines big data with food safety testing, with a view to providing reference for food safety regulatory authorities and promoting the healthy development of the food industry.